The purpose of this training grant is to equip the next generation of neuroscientists with the quantitative skills needed to keep pace with the rapid advances in neuroscientific methodology, and to make progress in understanding the complexity of systems-level function in the brain. While tremendous strides have been made in understanding the functions of single neurons, and their interactions in small assemblies, we still have relatively limited knowledge about how large scale computations performed by collections of neurons give rise to system-level behavior. Efforts to do so increasingly involve measurement techniques that generate large-scale data sets (e.g., Multiunit recordings, fMRI). Both the analysis of such data sets and, fundamentally, the ability to describe the complex dynamics of large populations of interacting neurons, require sophisticated quantitative methods. As in other domains of science, from physics and chemistry to population biology and economics, efforts to understand the behavior of systems made up of such large-scale, complex interactions have benefited by the application of formal theoretical techniques and advanced quantitative methods. These include mathematical tools such as information theory and nonlinear dynamical systems analysis, as well as computational modeling techniques. However, these conceptual tools are not routinely taught as a standard part of neuroscience training. The program we propose will seek to address this need. At the predoctoral level, we will aim to draw undergraduates with strong quantitative backgrounds (e.g., Majors in mathematics, physics, engineering and computer science, in addition to well trained biology and psychology majors) into neuroscience, in a way that allows them to continue to develop their quantitative skills in the context of a rigorous training in neuroscience. At the postdoctoral level, we will provide trained neuroscientists with in-depth experience in the use of mathematical and computational modeling tools. The program will bring faculty from ecology and evolutionary biology, molecular biology, and psychology (the three primary departments involved in neuroscience training) together with faculty in the program in applied and computational mathematics, as well as chemistry, engineering and philosophy, and prominent researchers at affiliated institutions in the Princeton area. The program will provide a directed curriculum of courses, a dedicated seminar series, annual retreat, and cutting-edge technology and laboratory facilities, all of which will be focused on providing trainees with the necessary conceptual tools for measuring, analyzing, and modeling complex neural dynamics at all levels.